{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:18:14Z","timestamp":1760055494042,"version":"build-2065373602"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>We performed this cross-sectional study to explore potential biomarkers for predicting diabetic kidney disease (DKD) in newly diagnosed type 2 diabetes mellitus (T2DM).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 623 patients were recruited from Xuzhou First People\u2019s Hospital and Nanjing First Hospital based on the electronic case records system. Patients were grouped according to their albuminuria-to-creatinine ratio (ACR) into two categories: ACR\u202f&amp;lt;\u202f30\u202fmg\/g and ACR\u202f\u2265\u202f30\u202fmg\/g. Biomarker levels between the two ACR groups were compared using an independent sample <jats:italic>t<\/jats:italic>-test. Correlation analysis was determined using Pearson\u2019s or Spearman\u2019s analysis and binary logistic regression. Receiver operating characteristic (ROC) curve analysis was used to elucidate the predictive effect of biomarkers on DKD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The levels of total cholesterol, glycated hemoglobin (HbA1c), fasting C-peptide, fasting insulin, and prevalence of hypertension were higher, while the levels of red blood cell counts (RBC), hemoglobin, ApoA, and free triiodothyronine were lower in the ACR\u202f\u2265\u202f30\u202fmg\/g group. Negative correlations were found between ACR\u202f\u2265\u202f30\u202fmg\/g and RBC, hemoglobin, albumin, and NAFLD (<jats:italic>r<\/jats:italic>\u202f=\u202f\u22120.094, <jats:italic>p<\/jats:italic>\u202f=\u202f0.02; <jats:italic>r<\/jats:italic>\u202f=\u202f\u22120.130, <jats:italic>p<\/jats:italic>\u202f=\u202f0.001; <jats:italic>r<\/jats:italic>\u202f=\u202f\u22120.137, <jats:italic>p<\/jats:italic>\u202f=\u202f0.001; <jats:italic>r<\/jats:italic>\u202f=\u202f\u22120.097, <jats:italic>p<\/jats:italic>\u202f=\u202f0.018), while positive correlations were found between high-density lipoprotein, fasting blood glucose, hypertension, and the ratio of 60-min postprandial insulin and serum apolipoprotein(a) (Ins60\/ApoA) (<jats:italic>r<\/jats:italic>\u202f=\u202f0.134, <jats:italic>p<\/jats:italic>\u202f=\u202f0.001; <jats:italic>r<\/jats:italic>\u202f=\u202f0.120, <jats:italic>p<\/jats:italic>\u202f=\u202f0.003; <jats:italic>r<\/jats:italic>\u202f=\u202f0.131, <jats:italic>p<\/jats:italic>\u202f=\u202f0.001; <jats:italic>r<\/jats:italic>\u202f=\u202f0.359, <jats:italic>p<\/jats:italic>\u202f=\u202f0.001). Furthermore, binary logistic regression showed that lnIns60\/ApoA was an independent influence factor for ACR\u202f\u2265\u202f30\u202fmg\/g. After adjusting for age, gender, hypertension, non-alcoholic fatty liver disease (NAFLD), hemoglobin, albumin, smoking history, alcohol consumption history, and body mass index (BMI), lnIns60\/ApoA was an independent influence factor for ACR\u202f\u2265\u202f30\u202fmg\/g(OR\u202f=\u202f2.778, <jats:italic>p<\/jats:italic>\u202f=\u202f0.015). The area under the ROC curve was 0.741 (95% CI: 0.629\u20130.854, <jats:italic>p<\/jats:italic>\u202f=\u202f0.001) for ACR\u202f\u2265\u202f30\u202fmg\/g. The analysis of ROC curves revealed that an optimal cutoff for ACR was 22.42\u202fmg\/g, with a sensitivity of 67.6% and a specificity of 72.1%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The ratio of Ins60\/ApoA could be used as an alternative biomarker for predicting DKD in newly diagnosed T2DM patients.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1569730","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T05:27:31Z","timestamp":1759987651000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel biomarker Ins60\/ApoA for predicting diabetic kidney disease in newly diagnosed type 2 diabetes: a pilot study"],"prefix":"10.3389","volume":"12","author":[{"given":"Jin","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengqing","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanwan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binbin","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"m997","DOI":"10.1136\/bmj.m997","article-title":"Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study","volume":"369","author":"Yongze","year":"2020","journal-title":"BMJ"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s41581-021-00490-8","article-title":"Mineralocorticoid receptor antagonists in diabetic kidney disease - mechanistic and therapeutic effects","volume":"18","author":"Barrera-Chimal","year":"2022","journal-title":"Nat Rev Nephrol"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1053\/j.ajkd.2019.05.032","article-title":"CKD in China: evolving Spectrum and public health implications","volume":"76","author":"Yang","year":"2020","journal-title":"Am J Kidney Dis"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1397160","DOI":"10.3389\/fmed.2024.1397160","article-title":"Biomarkers of chronic kidney disease in older individuals: navigating complexity in diagnosis","volume":"11","author":"Muglia","year":"2024","journal-title":"Front Med"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"98880","DOI":"10.5527\/wjn.v13.i4.98880","article-title":"Neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, and periostin: novel urinary biomarkers in diabetic nephropathy. 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